List all of the drawings in the rendered dataset

all_drawings = list.files(path='sketches_full_dataset', pattern = ".png$", recursive = TRUE)
all_drawings = as.tibble(all_drawings)
all_drawings$sketchName = all_drawings$value

Load csvs with descriptives

## We have slightly less than number of images in folder because it lost connection before finishing writing out the final csv file; only analyzing drawings in csv for now (number of actual all drawings changes every day since still getting data!)
descriptives_v4 = read_csv(file = 'mongodb-output/MuseumStation_AllDescriptives_7200_images_cdm_run_v4.csv', col_names = TRUE)%>%
  mutate(fileNameShort = str_split_fixed(filename,"/",11)[,11])
## Warning: Missing column names filled in: 'X1' [1]
## Parsed with column specification:
## cols(
##   X1 = col_integer(),
##   session_id = col_character(),
##   trial_num = col_integer(),
##   category = col_character(),
##   age = col_character(),
##   submit_time = col_double(),
##   submit_date = col_datetime(format = ""),
##   num_strokes = col_integer(),
##   draw_duration_old = col_double(),
##   draw_duration_new = col_double(),
##   trial_duration = col_double(),
##   mean_intensity = col_double(),
##   bounding_box = col_character(),
##   filename = col_character()
## )
## Warning: package 'bindrcpp' was built under R version 3.4.4
descriptives = read_csv(file = 'mongodb-output/MuseumStation_AllDescriptives_20780_images_final_cdm_run_v3.csv', col_names = TRUE) %>%
  mutate(fileNameShort = str_split_fixed(filename,"/",11)[,11]) %>%
  mutate(draw_duration_new = as.numeric(draw_duration_new)) %>%
  full_join(descriptives_v4)
## Warning: Missing column names filled in: 'X1' [1]
## Parsed with column specification:
## cols(
##   X1 = col_integer(),
##   session_id = col_character(),
##   trial_num = col_integer(),
##   category = col_character(),
##   age = col_character(),
##   submit_time = col_double(),
##   submit_date = col_datetime(format = ""),
##   num_strokes = col_integer(),
##   draw_duration_old = col_double(),
##   draw_duration_new = col_character(),
##   trial_duration = col_double(),
##   mean_intensity = col_double(),
##   bounding_box = col_character(),
##   filename = col_character()
## )
## Joining, by = c("X1", "session_id", "trial_num", "category", "age", "submit_time", "submit_date", "num_strokes", "draw_duration_old", "draw_duration_new", "trial_duration", "mean_intensity", "bounding_box", "filename", "fileNameShort")

How many drawings do we have before exclusion (not counting practice trials)?

#
practice_categories = c('shape','this circle','square','this square','something you love')
extra_prompt = c('something you love')

# 
raw_drawing_count <- descriptives_v4 %>%
  full_join(descriptives) %>%
  filter(!category %in% practice_categories) %>%
  filter(!category %in% extra_prompt)
## Joining, by = c("X1", "session_id", "trial_num", "category", "age", "submit_time", "submit_date", "num_strokes", "draw_duration_old", "draw_duration_new", "trial_duration", "mean_intensity", "bounding_box", "filename", "fileNameShort")
drawing_count_total = length(unique(raw_drawing_count$filename))

Load manual exclusions from previous filtering (incomplete)

## loading drawings to exclude based on manual exclusions
drawings_to_exclude = read_csv(file = 'images_to_exclude/images_to_exclude.txt', col_names = FALSE)
## Parsed with column specification:
## cols(
##   X1 = col_character()
## )
drawings_to_exclude = as.tibble(drawings_to_exclude)
drawings_to_exclude$fileNameShort = drawings_to_exclude$X1

Filter those drawings and make nicer columns

# filter out manually excluded drawings
descriptives_filtered_v1 <- descriptives %>%
  filter(!fileNameShort %in% drawings_to_exclude$X1) 

# only consider image paths with descriptives and that weren't filetered
filtered_drawing_paths <- all_drawings %>% 
  mutate(fileNameShort = str_split_fixed(sketchName,'/',2)[,2]) %>%
  filter(fileNameShort %in% descriptives$fileNameShort) %>%
  mutate(fullPath = paste("sketches_full_dataset/",sketchName,sep="")) %>%
  select(-value,-sketchName) 

descriptives_filtered_v2 <- descriptives_filtered_v1 %>%
  left_join(filtered_drawing_paths)
## Joining, by = "fileNameShort"

Filtering pass 1:

Grab images with weird intensities and save them out (won’t actually run this)

strokes_thres = quantile(descriptives_filtered_v2$num_strokes,.95)
light_thres = quantile(descriptives_filtered_v2$mean_intensity,.05)
dark_thres = quantile(descriptives_filtered_v2$mean_intensity,.95)

dark_drawings <- descriptives_filtered_v2 %>%
  filter(mean_intensity > dark_thres) 
  
almost_blank <- descriptives_filtered_v2 %>%
  filter(mean_intensity < light_thres)

dark_sketches_dir = 'sketches_subsets_inspection/maybe_exclude_dark_sketches' 
light_sketches_dir = 'sketches_subsets_inspection/maybe_exclude_light_sketches' 

# dir.create(dark_sketches_dir)
# dir.create(light_sketches_dir)
# 
# file.copy(dark_drawings$fullPath, dark_sketches_dir)
# file.copy(almost_blank$fullPath, light_sketches_dir)

After manually going through drawings and making new csv file, exclude those drawings

## loading drawings to exclude based on manual exclusions
drawings_to_exclude_2 = read_csv(file = 'images_to_exclude/images_to_exclude_scribbles_dark_light.txt', col_names = FALSE)
## Parsed with column specification:
## cols(
##   X1 = col_character()
## )
drawings_to_exclude_2 = as.tibble(drawings_to_exclude_2)

## copy all drawings into one directory
descriptives_filtered_v3 <- descriptives_filtered_v2 %>%
  filter(!fileNameShort %in% drawings_to_exclude_2$X1) 

# filtered_sketches_dir = 'filtered_sketches'
# dir.create(filtered_sketches_dir)
# file.copy(descriptives_full_filtered$fullPath, filtered_sketches_dir)
## loading drawings to exclude based on manual exclusions based on rest of dataset
drawings_to_exclude_3 = read_csv(file = 'images_to_exclude/images_to_exclude_manual.txt', col_names = FALSE)
## Parsed with column specification:
## cols(
##   X1 = col_character()
## )
drawings_to_exclude_3 = as.tibble(drawings_to_exclude_3)

Exclude last round and clean up datasets

## Clean up -- exclude practice categories, recode age as numeric, make filenames w/o spaces
practice_categories = c('shape','this circle','square','this square','something you love')
extra_prompt = c('something you love')

# clean up dataset and filter out manual exclusions
descriptives_filtered_v4 <- descriptives_filtered_v3 %>%
  filter(!fileNameShort %in% drawings_to_exclude_3$X1) %>%
  mutate(age_numeric = as.numeric(str_split_fixed(age,'age',2)[,2])) %>%
  mutate(fileNameShortNew = str_split_fixed(fileNameShort," ",2)[,2]) %>%
  mutate(version = as.numeric(is.na(draw_duration_new))) 

# for object drawing trials
descriptives_full_filtered <- descriptives_filtered_v4 %>%
  filter(!category %in% practice_categories) %>%
  filter(!category %in% extra_prompt) 

## now only get practice trials for people who made it into the full dataset
obj_session_ids = unique(descriptives_full_filtered$session_id)
practice_shapes_filtered <- descriptives_filtered_v4 %>%
  filter(category %in% practice_categories) %>%
  filter(!category %in% extra_prompt) %>%
  filter(session_id %in% obj_session_ids)

# save output csvs
dir.create('filtered_dataset/output-csvs')
## Warning in dir.create("filtered_dataset/output-csvs"): 'filtered_dataset/
## output-csvs' already exists
write.csv(practice_shapes_filtered, "filtered_dataset/output-csvs/practice_shapes_filtered_nov19.csv")
write.csv(descriptives_full_filtered, "filtered_dataset/output-csvs/cdm_descriptives_nov19_filtered.csv")

Render out new copies of filtered datasets

## Render out new copy of practice shapes
pracImagesDir = 'practice_shapes_filtered'
dir.create(pracImagesDir)
## Warning in dir.create(pracImagesDir): 'practice_shapes_filtered' already
## exists
file.copy(practice_shapes_filtered$fullPath, to = file.path(pracImagesDir,practice_shapes_filtered$fileNameShort))
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# ## Render out new copy of filtered dataset / rename shapes
# dir.create('dataset_nov19/sketches_filtered_copy')
# dir.create('dataset_nov19/sketches_filtered_renamed')
# file.copy(from = descriptives_full_filtered$fullPath, to = 'dataset_nov19/sketches_filtered_copy')
# file.rename(from = file.path('dataset_nov19/sketches_filtered_copy',descriptives_full_filtered$fileNameShort),to=file.path('dataset_nov19/sketches_filtered_renamed',descriptives_full_filtered$fileNameShortNew))

How many sketches are we left with in each age / category?

category_by_age <- descriptives_full_filtered %>%
  group_by(category,age_numeric) %>%
  summarize(countImages = n())

ggplot(category_by_age,aes(age_numeric,countImages,color=age_numeric)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  facet_wrap(~category)

Look at how many exclusions we made in diferent categories / ages

all_exclusions <- drawings_to_exclude %>%
  full_join(drawings_to_exclude_2) %>%
  full_join(drawings_to_exclude_3) %>%
  rename(sketchName = X1) %>%
  mutate(category = str_split_fixed(sketchName,'_',4)[,1]) %>%
  mutate(age = str_split_fixed(sketchName,'_',4)[,3]) %>%
  mutate(age_numeric = str_split_fixed(age,'age',2)[,2])
## Joining, by = "X1"
## Joining, by = "X1"
exclusion_summary <- all_exclusions %>%
  group_by(age_numeric, category) %>%
  summarise(numExclusions = n())

ggplot(exclusion_summary,aes(age_numeric,numExclusions,color=age_numeric)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  facet_wrap(~category)

drawing_count_exclusions = length(all_exclusions$sketchName)
exclusion_percent= drawing_count_exclusions/ drawing_count_total

We had 15594 excluding practice trials, and we excluded 3711 drawings, or 0.2379761% of drawings.

How do intensity, # of strokes, and drawing duration vary with age / category?

ggplot(descriptives_full_filtered,aes(age_numeric,mean_intensity,color=category)) +
  geom_boxplot(aes(group=age_numeric)) +
  facet_wrap(~category) 

ggplot(descriptives_full_filtered,aes(age_numeric,mean_intensity,color=category)) +
  geom_point(alpha=.2) 

ggplot(descriptives_full_filtered,aes(age_numeric,draw_duration_old,color=category)) +
  geom_boxplot(aes(group=age_numeric)) +
  facet_wrap(~category) 

ggplot(descriptives_full_filtered,aes(age_numeric,num_strokes,color=age_numeric)) +
  geom_boxplot(aes(group=age_numeric)) +
  facet_wrap(~category) 

# now limit the y-axis so we can see without outliers 
ggplot(descriptives_full_filtered,aes(age_numeric,num_strokes,color=age_numeric)) +
  geom_boxplot(aes(group=age_numeric)) +
  facet_wrap(~category) +
  ylim(c(0, 100))
## Warning: Removed 37 rows containing non-finite values (stat_boxplot).

Hm, there are some drawings with lots of strokes – lets look at them.

# look at the crazy drawings
high_strokes <- descriptives_full_filtered %>%
  filter(num_strokes > 500)

dir.create('sketches_subsets_inspection/images_many_strokes')
## Warning in dir.create("sketches_subsets_inspection/images_many_strokes"):
## 'sketches_subsets_inspection/images_many_strokes' already exists
file.copy(high_strokes$fullPath, 'sketches_subsets_inspection/images_many_strokes')
## [1] FALSE FALSE
# make a plot to look at aveages by age /category
high_strokes_sum <- descriptives_full_filtered %>%
  group_by(version,category,age_numeric) %>%
  summarise(mean_strokes = mean(num_strokes))

ggplot(high_strokes_sum,aes(age_numeric,mean_strokes,color=version)) +
  geom_point()

# output some summary stats
high_strokes_by_version <- high_strokes_sum %>%
  group_by(version) %>%
  summarise(mean_strokes_v = mean(mean_strokes), sd_strokes_v = sd(mean_strokes))

Timing info

## all drawings
effort_sum <- descriptives_full_filtered %>%
  group_by(category,age_numeric) %>%
  summarise(mean_strokes = mean(num_strokes), mean_intensity = mean(mean_intensity), draw_duration = mean(draw_duration_old))

## only cdm_run_v4, which has new timing info
effort_sum_v4 <- descriptives_full_filtered %>%
  filter(version==0) %>%
  group_by(category,age_numeric) %>%
  summarise(mean_strokes = mean(num_strokes), mean_intensity = mean(mean_intensity), draw_duration = mean(draw_duration_new))
# weird <- descriptives_full_filtered %>% filter(draw_duration_old >30)
# dir.create('sketches_subsets_inspection/long_duration')
# file.copy(weird$fullPath, 'sketches_subsets_inspection/long_duration')
# 
# long_trials <- descriptives_full_filtered %>% 
#   filter(version==0) %>%
#   filter(trial_duration>31) 
# 
# dir.create('sketches_subsets_inspection/long_trial_duration')
# file.copy(long_trials$fullPath, 'sketches_subsets_inspection/long_trial_duration')

Make some plots of these basic descriptives across age after averaging

ggplot(effort_sum,aes(age_numeric,mean_intensity,color=category)) +
  geom_point() +
  facet_wrap(~category) +
  geom_smooth(alpha=.1)
## `geom_smooth()` using method = 'loess'

ggplot(effort_sum,aes(age_numeric,mean_strokes,color=category)) +
  geom_point() +
  facet_wrap(~category) +
  geom_smooth(alpha=.1)
## `geom_smooth()` using method = 'loess'

ggplot(effort_sum,aes(age_numeric,draw_duration,color=category)) +
  geom_point() +
  facet_wrap(~category) +
  geom_smooth(alpha=.1)
## `geom_smooth()` using method = 'loess'